暂无分享,去创建一个
Petros Daras | Dimitrios Zarpalas | Nikolaos Zioulis | Petros Drakoulis | Federico Alvarez | Georgios Albanis | P. Daras | N. Zioulis | Federico Álvarez | D. Zarpalas | G. Albanis | Petros Drakoulis
[1] Marco Federici,et al. An Information-theoretic Approach to Distribution Shifts , 2021, NeurIPS.
[2] P. Daras,et al. Pano3D: A Holistic Benchmark and a Solid Baseline for 360° Depth Estimation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[3] Eric Eaton,et al. Towards a theory of out-of-distribution learning , 2021, ArXiv.
[4] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[5] Abhimanyu Dubey,et al. Adaptive Methods for Real-World Domain Generalization , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Jitao Sang,et al. Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data , 2021, ArXiv.
[7] Yixuan Li,et al. Generalized Out-of-Distribution Detection: A Survey , 2021, ArXiv.
[8] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[9] Friedrich Fraundorfer,et al. Evaluation of CNN-based Single-Image Depth Estimation Methods , 2018, ECCV Workshops.
[10] Behnam Neyshabur,et al. The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning , 2021, Trans. Mach. Learn. Res..
[11] Claes Lundström,et al. A Closer Look at Domain Shift for Deep Learning in Histopathology , 2019, ArXiv.
[12] Matthias Nießner,et al. Matterport3D: Learning from RGB-D Data in Indoor Environments , 2017, 2017 International Conference on 3D Vision (3DV).
[13] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[14] Li Fei-Fei,et al. Progressive Neural Architecture Search , 2017, ECCV.
[15] Steven G. McDonagh,et al. On the Out-of-distribution Generalization of Probabilistic Image Modelling , 2021, NeurIPS.
[16] Rob Fergus,et al. Depth Map Prediction from a Single Image using a Multi-Scale Deep Network , 2014, NIPS.
[17] Konrad Schindler,et al. Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[18] Takayuki Okatani,et al. Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps With Accurate Object Boundaries , 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).
[19] Ali Taylan Cemgil,et al. A Fine-Grained Analysis on Distribution Shift , 2021, ICLR.
[20] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[21] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[22] Peng Cui,et al. Towards Out-Of-Distribution Generalization: A Survey , 2021, ArXiv.
[23] Silvio Savarese,et al. Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments , 2020, IEEE Robotics and Automation Letters.
[24] Chunhua Shen,et al. Enforcing Geometric Constraints of Virtual Normal for Depth Prediction , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[25] Jan-Michael Frahm,et al. VPLNet: Deep Single View Normal Estimation With Vanishing Points and Lines , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Jure Leskovec,et al. WILDS: A Benchmark of in-the-Wild Distribution Shifts , 2021, ICML.